方法对比
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| 普通最小二乘法 (OLS) 回归× | 光滑转换自回归 (STAR) 模型× | 系统GMM（Arellano-Bover / Blundell-Bond）× | |
|---|---|---|---|
| 领域 | 计量经济学 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model | Regression model |
| 起源年份≠ | 2019 | 1994 | 1998 |
| 提出者≠ | Wooldridge (textbook treatment); classical least squares | Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002) | Arellano & Bover (1995); Blundell & Bond (1998) |
| 类型≠ | Linear regression | Nonlinear time-series regime-switching model | Dynamic panel data estimator |
| 开创性文献≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218. DOI ↗ | Arellano, M. & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Review of Economic Studies, 58(2), 277-297. DOI ↗ |
| 别名≠ | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR | Arellano-Bover estimator, Blundell-Bond estimator, dynamic panel GMM, Sistem GMM (Arellano-Bover / Blundell-Bond) |
| 相关≠ | 5 | 4 | 4 |
| 摘要≠ | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). | The Smooth Transition Autoregressive (STAR) model is a nonlinear time-series model, developed in Teräsvirta's 1994 framework, that lets the dynamics move smoothly rather than abruptly between two regimes. The logistic variant (LSTAR) captures asymmetric business cycles and the exponential variant (ESTAR) captures purchasing-power-parity deviations. | System GMM is a generalized method of moments estimator for dynamic panel models that contain a lagged dependent variable. Introduced by Blundell and Bond (1998), building on Arellano and Bover, it augments the differenced equation of the earlier difference GMM (Arellano-Bond) with the equation in levels to deliver consistent estimates when N is large and T is small. |
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